MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines

Authors:
Daniil Chivilikhin;Vladimir Ulyantsev
Affiliations:
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Fed.;Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Fed.
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

In this paper we present MuACOsm - a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM. The goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem.